Large-scale computational modelling of H5 influenza variants against HA1-neutralising antibodies

BACKGROUND

The United States Department of Agriculture has recently released reports that show samples collected from 2022 to 2025 of highly pathogenic avian influenza (H5N1) have been detected in mammals and birds. Up to February 2025, the United States Centres for Disease Control and Prevention reports that there have been 67 humans infected with H5N1 since 2024 with 1 death. The broader potential impact on human health remains unclear.

METHODS

In this study, we computationally model 1804 protein complexes consisting of various H5 isolates from 1959 to 2024 against 11 haemagglutinin domain 1 (HA1)-neutralising antibodies. This was performed using AI-based protein folding and physics-based simulations of the antibody-antigen interactions. We analysed binding affinity changes over time and across various antibodies using multiple biochemical and biophysical binding metrics.

FINDINGS

This study shows a trend of weakening binding affinity of existing antibodies against H5 isolates over time, indicating that the H5N1 virus is evolving immune escape from our therapeutic and immunological defences. We also found that based on the wide variety of host species and geographic locations in which H5N1 was observed to have been transmitted from birds to mammals, there is not a single central reservoir host species or location associated with H5N1's spread.

INTERPRETATION

These results indicate that the virus has potential to move from epidemic to pandemic status. This study illustrates the value of high-performance computing to rapidly model protein-protein interactions and viral genomic sequence data at-scale for functional insights into medical preparedness.

FUNDING

No external funding was used in this study.

Production of a monoclonal antibody targeting the SARS-CoV-2 Omicron spike protein and analysis of SARS-CoV-2 Omicron mutations related to monoclonal antibody resistance

SARS-CoV-2 mutations have resulted in the emergence of multiple concerning variants, with Omicron being the dominant strain presently. Therefore, we developed a monoclonal antibody (mAb) against the spike (S) protein of SARS-CoV-2 Omicron for therapeutic applications. We established the 1E3H12 mAb, recognizing the receptor binding domain (RBD) of the Omicron S protein, and found that the 1E3H12 mAb can efficiently recognize the Omicron S protein with weak affinity to the Alpha, Beta, and Mu variants, but not to the parental strain and Delta variant. Based on in vitro assays, the mAb demonstrated neutralizing activity against Omicron BA.1, BA.4/5, BQ.1.1, and XBB. A humanized antibody was further produced and proved to have neutralizing activity. To verify the potential limitations of the 1E3H12 mAb due to viral escape of SARS-CoV-2 Omicron variants, we analyzed the emergence of variants by whole genome deep sequencing after serial passage in cell culture. The results showed a few unique S protein mutations in the genome associated with resistance to the mAb. These findings suggest that this antibody not only contributes to the therapeutic arsenal against COVID-19 but also addresses the ongoing challenge of antibody resistance among the evolving subvariants of SARS-CoV-2 Omicron.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Nucleocapsid Protein Variants

The COVID-19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until 23 July 2023. Since the three-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted co-operatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.

Human cytokine and coronavirus nucleocapsid protein interactivity using large-scale virtual screens

Understanding the interactions between SARS-CoV-2 and the human immune system is paramount to the characterization of novel variants as the virus co-evolves with the human host. In this study, we employed state-of-the-art molecular docking tools to conduct large-scale virtual screens, predicting the binding affinities between 64 human cytokines against 17 nucleocapsid proteins from six betacoronaviruses. Our comprehensive in silico analyses reveal specific changes in cytokine-nucleocapsid protein interactions, shedding light on potential modulators of the host immune response during infection. These findings offer valuable insights into the molecular mechanisms underlying viral pathogenesis and may guide the future development of targeted interventions. This manuscript serves as insight into the comparison of deep learning based AlphaFold2-Multimer and the semi-physicochemical based HADDOCK for protein-protein docking. We show the two methods are complementary in their predictive capabilities. We also introduce a novel algorithm for rapidly assessing the binding interface of protein-protein docks using graph edit distance: graph-based interface residue assessment function (GIRAF). The high-performance computational framework presented here will not only aid in accelerating the discovery of effective interventions against emerging viral threats, but extend to other applications of high throughput protein-protein screens.

Structure-based approaches against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has had a major impact on human life. This review highlights the versatile roles of both classical and modern structure-based approaches for COVID-19. X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryogenic electron microscopy are the three cornerstones of classical structural biology. These technologies have helped provide fundamental and detailed knowledge regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the related human host proteins as well as enabled the identification of its target sites, facilitating the cessation of its transmission. Further progress into protein structure modeling was made using modern structure-based approaches derived from homology modeling and integrated with artificial intelligence (AI), facilitating advanced computational simulation tools to actively guide the design of new vaccines and the development of anti-SARS-CoV-2 drugs. This review presents the practical contributions and future directions of structure-based approaches for COVID-19.

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tumor cells by the immune system. While there are already multiple FDA-approved anti-PD-1 antibodies, including nivolumab (Opdivo® from Bristol-Myers Squibb) and pembrolizumab (Keytruda® from Merck), there are ongoing efforts to discover new and improved checkpoint inhibitor therapeutics. In this study, we present multiple anti-PD-1 antibody fragments that were derived computationally using protein diffusion and evaluated through our scalable, in silico pipeline. Here we present nine synthetic Fv structures that are suitable for further empirical testing of their anti-PD-1 activity due to desirable predicted binding performance.

De novo design of a stapled peptide targeting SARS-CoV-2 spike protein receptor-binding domain

Although effective vaccines have been developed against SARS-CoV-2, many regions in the world still have low rates of vaccination and new variants with mutations in the viral spike protein have reduced the effectiveness of most available vaccines and treatments. There is an urgent need for a drug to cure this disease and prevent infection. The SARS-CoV-2 virus enters the host cell through protein-protein interaction between the virus's spike protein and the host's angiotensin converting enzyme (ACE2). Using protein design software and molecular dynamics simulations, we have designed a 17-residue peptide (pep39), that binds to the spike protein receptor-binding domain (RBD) and blocks interaction of spike protein with ACE2. We have confirmed the binding activity of the designed peptide for the original spike protein and the delta variant spike protein using micro-cantilever and bio-layer interferometry (BLI) based methods. We also confirmed that pep39 strongly inhibits SARS-CoV-2 virus replication in Vero E6 cells. Taken together these data suggest that a newly designed spike protein RBD blocking peptide pep39 has a potential as a SARS-CoV-2 virus inhibitor.

Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

SARS-CoV-2 Spike Protein Post-Translational Modification Landscape and Its Impact on Protein Structure and Function via Computational Prediction

To elucidate the role of post-translational modifications (PTMs) in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein's structure and virulence, we generated a high-resolution map of 87 PTMs using liquid chromatography with tandem mass spectrometry data on the extracted spike protein from SARS-CoV-2 virions and then reconstituted its structure heterogeneity caused by PTMs. Nonetheless, Alphafold2, a high-accuracy artificial intelligence tool to perform protein structure prediction, relies solely on primary amino acid sequence, whereas the impact of PTM, which often modulates critical protein structure and function, is much ignored. To overcome this challenge, we proposed the mutagenesis approach-an in silico, site-directed amino acid substitution to mimic the influence of PTMs on protein structure due to altered physicochemical properties in the post-translationally modified amino acids-and then reconstituted the spike protein's structure from the substituted sequences by Alphafold2. For the first time, the proposed method revealed predicted protein structures resulting from PTMs, a problem that Alphafold2 has yet to address. As an example, we performed computational analyses of the interaction of the post-translationally modified spike protein with its host factors such as angiotensin-converting enzyme 2 to illuminate binding affinity. Mechanistically, this study suggested the structural analysis of post-translationally modified protein via mutagenesis and deep learning. To summarize, the reconstructed spike protein structures showed that specific PTMs can be used to modulate host factor binding, guide antibody design, and pave the way for new therapeutic targets. The code and Supplementary Materials are freely available at https://github.com/LTZHKUSTGZ/SARS-CoV-2-spike-protein-PTM.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

Conformational Dynamics of the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein

Variants of SARS-CoV-2 keep emerging and causing new waves of COVID-19 around the world. Effective new approaches in drug development are based on the binding of agents, such as neutralizing monoclonal antibodies to a receptor-binding domain (RBD) of SARS-CoV-2 spike protein. However, mutations in RBD may lower the affinity of previously developed antibodies. Therefore, rapid analysis of new variants and selection of a binding partner with high affinity is of great therapeutic importance. Here, we explore a computational approach based on molecular dynamics simulations and conformational clusterization techniques for the wild-type and omicron variants of RBD. Biochemical experiments support the hypothesis of the presence of several conformational states within the RBD assembly. The development of such an approach will facilitate the selection of neutralization drugs with higher affinity based on the primary structure of the target antigen.

A bias of Asparagine to Lysine mutations in SARS-CoV-2 outside the receptor binding domain affects protein flexibility

Introduction

COVID-19 pandemic has been threatening public health and economic development worldwide for over two years. Compared with the original SARS-CoV-2 strain reported in 2019, the Omicron variant (B.1.1.529.1) is more transmissible. This variant has 34 mutations in its Spike protein, 15 of which are present in the Receptor Binding Domain (RBD), facilitating viral internalization via binding to the angiotensin-converting enzyme 2 (ACE2) receptor on endothelial cells as well as promoting increased immune evasion capacity.

Methods

Herein we compared SARS-CoV-2 proteins (including ORF3a, ORF7, ORF8, Nucleoprotein (N), membrane protein (M) and Spike (S) proteins) from multiple ancestral strains. We included the currently designated original Variant of Concern (VOC) Omicron, its subsequent emerged variants BA.1, BA2, BA3, BA.4, BA.5, the two currently emerging variants BQ.1 and BBX.1, and compared these with the previously circulating VOCs Alpha, Beta, Gamma, and Delta, to better understand the nature and potential impact of Omicron specific mutations.

Results

Only in Omicron and its subvariants, a bias toward an Asparagine to Lysine (N to K) mutation was evident within the Spike protein, including regions outside the RBD domain, while none of the regions outside the Spike protein domain were characterized by this mutational bias. Computational structural analysis revealed that three of these specific mutations located in the central core region, contribute to a preference for the alteration of conformations of the Spike protein. Several mutations in the RBD which have circulated across most Omicron subvariants were also analysed, and these showed more potential for immune escape.

Conclusion

This study emphasizes the importance of understanding how specific N to K mutations outside of the RBD region affect SARS-CoV-2 conformational changes and the need for neutralizing antibodies for Omicron to target a subset of conformationally dependent B cell epitopes.

Omicron variant: Current insights and future directions

The global COVID-19 outbreak has returned with the identification of the SARS-CoV-2 Omicron variant (B.1.1.529) after appearing to be persistently spreading for the more than past two years. In comparison to prior SARS-CoV-2 variants, this new variant revealed a significant amount of mutation. This novel variety may have a greater rate of transmissibility which might impede the effectiveness of current diagnostic equipment as well as vaccination efficacy and also impede immunotherapies (Antibody / monoclonal antibody based). WHO designated B.1.1.529 as a variant of concern on November 26, 2021, identified as Omicron. The Omicron variant transmission method and severity, on the other hand, are well defined. The global spread of Omicron, which has now seized many nations, has resulted in numerous speculations regarding its origin and degree of infectivity. The following sections will go over its potential for transmission, omicron structure, and impact on COVID-19 vaccines, how it is different from delta variant and diagnostics.

Heterologous Prime-boost of SARS-CoV-2 inactivated vaccine and mRNA BNT162b2 among Healthy Thai Adolescents

Background

Heterologous prime-boost SARS-CoV-2 vaccination is a widely accepted strategy during the COVID-19 pandemic, which generated a superior immune response than homologous vaccination strategy.

Objective

To describe immunogenicity of heterologous prime-boost vaccination with inactivated vaccine, CoronaVac, followed by BNT162b2 and 5-month booster dose with BNT162b2 in healthy Thai adolescents.

Methods

Adolescents aged 12-18 years were randomized 1:1:1:1 to receive CoronaVac (SV) followed by BNT162b2 (PZ) 30 or 20 µg at either 3- or 6-week interval (SV3w/PZ30µg, SV3w/PZ20µg, SV6w/PZ30µg or SV6w/PZ20µg). During the Omicron-predominant period, participants were offered a BNT162b2 booster dose 30, 15, or 10 µg. Immunogenicity was determined using IgG antibody against spike-receptor-binding domain of wild type(anti-S-RBD IgG) and surrogate virus neutralization test(sVNT) against Delta variant at 14 days and 5 months after the 2nd dose. Neutralization tests(sVNT and pseudovirus neutralization test; pVNT) against Omicron strain were tested pre- and 14 days post-booster dose.

Results

In October 2021, 76 adolescents with a median age of 14.3 years (IQR 12.7-16.0) were enrolled: 20 in SV3w/PZ30µg; 17 in SV3w/PZ20µg; 20 in SV6w/PZ30µg; 19 in SV6w/PZ20µg. At day 14, the geometric mean(GM) of anti-S-RBD IgG in SV3w/PZ30µg was 4713 (95 %CI 4127-5382) binding-antibody unit (BAU)/ml, while geometric mean ratio(GMR) was 1.28 (1.09-1.51) in SV6w/PZ30µg. The GMs of sVNT against Delta variants at day 14 among participants in SV3w/PZ30µg and SV6wk/PZ30µg arm were 95.3 % and 99.7 %inhibition, respectively. At 5 months, GMs of sVNT against Delta variants in SV3w/PZ30µg were significantly declined to 47.8 % but remained at 89.0 % inhibition among SV6w/PZ30µg arm. In April 2022, 52 adolescents received a BNT162b2 booster dose. Proportion of participants with sVNT against Omicron strain > 80 %inhibition was significantly increased from 3.8 % pre-booster to 67 % post-booster. Proportion of participants with pVNT ID50 > 185 was 42 % at 14 days post 2nd dose and 88 % post booster, respectively.

Conclusions

Heterologous prime-boost vaccination with CoronaVac followed by BNT162b2 induced high neutralizing titer against SARS-CoV-2 Delta strain. After 5-month interval, booster with BNT162b2 induced high neutralizing titer against Omicron strain.Thai Clinical Trials Registry (thaiclinicaltrials.org): TCTR20210923012.

Electrostatic Features for the Receptor Binding Domain of SARS-COV-2 Wildtype and Its Variants. Compass to the Severity of the Future Variants with the Charge-Rule

Electrostatic intermolecular interactions are important in many aspects of biology. We have studied the main electrostatic features involved in the interaction of the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein with the human receptor Angiotensin-converting enzyme 2 (ACE2). As the principal computational tool, we have used the FORTE approach, capable to model proton fluctuations and computing free energies for a very large number of protein-protein systems under different physical-chemical conditions, here focusing on the RBD-ACE2 interactions. Both the wild-type and all critical variants are included in this study. From our large ensemble of extensive simulations, we obtain, as a function of pH, the binding affinities, charges of the proteins, their charge regulation capacities, and their dipole moments. In addition, we have calculated the pK_as for all ionizable residues and mapped the electrostatic coupling between them. We are able to present a simple predictor for the RBD-ACE2 binding based on the data obtained for Alpha, Beta, Gamma, Delta, and Omicron variants, as a linear correlation between the total charge of the RBD and the corresponding binding affinity. This "RBD charge rule" should work as a quick test of the degree of severity of the coming SARS-CoV-2 variants in the future.

Omicron: Understanding the Latest Variant of SARS-CoV-2 and Strategies for Tackling the Infection

The new variant of concern of SARS-CoV-2, namely Omicron, has triggered global fear recently. To date, our knowledge of Omicron, particularly of how S glycoprotein mutations affect the infectivity of the virus and the severity of the infection, is far from complete. This hinders our ability to treat the disease and to predict the future state of SARS-CoV-2 threats to well-being and economic stability. Despite this, efforts have been made to unveil the routes of transmission and the efficiency of existing vaccines in tackling Omicron. This article reviews the latest understanding of Omicron and the current status of the use of vaccines and drugs for infection control. It is hoped that this article can offer insights into the development of more effective measures to tackle the pandemic.

Omicron variant receptor-binding domain phylogenetics and molecular dynamics

BACKGROUND

We investigated the evolutionary relationships, mutations, antigenic epitopes, and structural dynamics of the receptor-binding domain (RBD) of SARS-CoV-2, Omicron and other recently evolved variants.

METHODS

The RBD of SARS-CoV-2 and its Omicron, Alpha, Beta, Gamma, Delta, and Mu variants were subjected to pairwise sequence matrix evaluation, antigenic epitope prediction, and phylogenetic relationship and structural dynamics analyses.

RESULTS

The Omicron RBD contained 13-15 amino acid mutations, of which 12 were new and three conserved with other variants. In addition, two mutations found in the Alpha, Beta, Gamma, and Mu variants were not found in the Omicron RBD. The ultrametric clustering unweighted pair group method with arithmetic mean identified Omicron as a novel monophyletic class, but the neighbor-joining method clustered Omicron with Alpha and Delta variants. In the SARS-CoV-2 RBD, five main antigenic epitopes were predicted, and these epitopes were conserved across all SARS-CoV-2 variants tested. Surprisingly, the additional mutations in the Omicron variant increased the size of the expected antigenic sites in two of these antigenic epitopes. Molecular dynamics (MD) simulations revealed higher root-mean-square deviation in the Omicron RBD, greater residue fluctuation at residues 32-42 and 140-160, and increased solvent-accessible surface area.

CONCLUSIONS

The Omicron RBD mutations indicate the variant is within a new phylogenetic class of SARS-CoV-2 and significantly impact RBD structure, conformation, and molecular dynamics. However, conserved anticipated antigenic sites may imply partial changes in receptor affinity and response to immune reactions. Omicron RBD binding with the angiotensin-converting enzyme 2 receptor was suggested to be weaker than the original SARS-CoV-2 binding in MD simulations.

A Randomized Clinical Trial of a Fractional Low Dose of BNT162b2 Booster in Adults Following AZD1222

In the era of globally predominant omicron strains, a COVID-19 booster vaccine is needed. Our study aimed to evaluate the immunogenicity of a half-dose BNT162b2 booster after AZD1222 in healthy adults. A randomized trial of volunteers aged 18-69 years who received two-dose AZD1222 was conducted. The participants were randomized to receive the BNT162b2 vaccine intramuscularly-half (15 µg) vs. standard dose (30 µg). The immunogenicity was evaluated by a surrogate virus neutralization test (sVNT) against omicron variants and anti-spike-receptor-binding-domain IgG (anti-S-RBD IgG). From November-December 2021, 100 adults with a median age of 59.3 years (IQR 33.4-65.5) were enrolled. A booster dose was given at median of 98 days (IQR 92-128) after AZD1222. At day 14, the geometric means (GMs) of anti-S-RBD IgG in half- vs. standard-dose group were 2329.8 vs. 2574.7 BAU/mL, with a geometric mean ratio (GMR) of 0.90 (0.77-1.06). The GMs of sVNT against the omicron variant in the half- and standard-dose groups were 74.4% inhibition (95% CI 68.8-80.5) and 67.3% inhibition (57.9-78.1), respectively, with GMR of 0.95 (0.69-1.30). At day 90, the sVNT indicated 22.3% inhibition (95% CI 14.9-33.4) and 20.4% inhibition (13.1-32.0), respectively, with GMR of 1.09 (0.60-1.98). The fractional low-dose BNT162b2 mRNA booster vaccine provided non-inferior immunogenicity responses. During a shortage of vaccine supply, a fractional low dose should be considered for a booster vaccination program.

Identifying vaccine escape sites via statistical comparisons of short-term molecular dynamics

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamics (MD)-based approach that goes beyond contact mapping, scales well to a desktop computer with a modern graphics processor, and enables the user to identify functional protein sites that are prone to vaccine escape in a viral antigen. We first implement our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs thus enabling a statistical comparison of the rapid motion of influenza hemagglutinin (HA) in both the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in influenza HA, predetermined via selection experiments and neutralization assays. Upon the validation of our computational approach, we then surveyed potential hotspot residues in the receptor binding domain of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified many single sites in the antigen-antibody interface that are similarly prone to potential antibody escape and that match many of the known sites of mutations arising in the SARS-CoV-2 variants of concern. In the Omicron variant, we find only minimal adaptive evolutionary shifts in the functional binding profiles of both antibodies. In summary, we provide an inexpensive and accurate computational method to monitor hotspots of functional evolution in antibody binding footprints.

Understanding T-cell responses to COVID-19 is essential for informing public health strategies

Durable T-cell responses to SARS-CoV-2 antigens after infection or vaccination improve immune-mediated viral clearance. To date, population-based surveys of COVID-19 adaptive immunity have focused on testing for IgG antibodies that bind spike protein and/or neutralize the virus. Deployment of existing methods for measuring T-cell immunity could provide a more complete profile of immune status, informing public health policies and interventions.